# Plotting

=== "Packages you will learn to use"
    1. Fast professional static plots: ggplot2 and rpy2
    2. Complicated static plots: matplotlib
    3. Live stream and/or interactive plots: plotly
    4. Dashboard: streamlit

Typically with tremendous mastery of matplotlib, you will be able to create any good looking plots static or interactive. However, it takes many more lines of code and is relatively complicated.

Thus, before we go there, we will be covering the fastest way to create professional looking static plots with rpy2 and ggplot rapidly. It is unconventional but it yields the fastest result for time-sensitive learners. We'll then move backwards to then create more complicated plots with matplotlib. Subsequently we'll create live stream and interactive plots with plotly. Finally we will be bringing everything together with streamlit as a dashboard.

As usual, you can go freestyle and just use any of our open-source material you think is useful and you do not need to follow the sequence we are suggesting.
